With its unparalleled flexibility, rapid development and cost-saving capabilities, open source is proving time and again that it’s the leader in data management. But as the growth in open source adoption increases, so does the complexity of your data infrastructure. In this Analyst Brief developed with IDC, discover how and why the best solution to this complexity is a managed service, including: Streamlined compliance with some of the most complex regulatory guidelines Simplified operations, li

Adding high-quality entity resolution capabilities to enterprise applications, services, data fabrics or data pipelines can be daunting and expensive. Organizations often invest millions of dollars and years of effort to achieve subpar results. This guide will walk you through the requirements and challenges of implementing entity resolution. By the end, you'll understand what to look for, the most common mistakes and pitfalls to avoid, and your options.

With no barriers to entry you can get started with Snowflake for next to nothing, but as you may already know, costs can quickly spiral out of control. While usage costs can be better managed for your internal BI use case, Snowflake costs skyrocket for SaaS providers because the need to deliver real-time, interactive analytics in a multi-tenant environment is always on.

Without proper guidance, it’s easy to miss out on Kafka’s full capabilities. While not the easiest technology to optimize, Kafka rewards those willing to explore its depths. Under the hood, it is an elegant system for stream processing, event sourcing, and data integration. Download this white paper to learn the 10 critical rules that will help you optimize your Kafka system and unlock its full potential.

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

Multi-tenant architecture allows software vendors to realize tremendous efficiencies by maintaining a single application stack instead of separate database instances while meeting data privacy needs. When you use a data warehouse to power your multi-tenant analytics, the proper approach is vital. Multi-tenant analytics is NOT the primary use case with traditional data warehouses, causing data security challenges.

It’s no surprise that Apache Cassandra has emerged as a popular choice for organizations of all sizes seeking a powerful solution to manage their data at a scale—but with great power comes great responsibility. Due to the inherent complexity of distributed databases, this white paper will uncover the 10 rules you’ll want to know when managing Apache Cassandra.

Embedded analytics can help you deliver cutting-edge analytics experiences to your end-users that align with KPIs that are critical to the growth and success of your business. Read this eBook to learn how an embedded analytics platform, like Qrvey, can help PMs exceed the following KPIs: Growing revenue while improving customer retention rate Delivering rapid time to value Earning a high net-promoter score Increasing Gross Margin / Profitability Conversion rate from trial to paid Don’t just meet

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

Apache Cassandra is an open-source distributed database that boasts an architecture that delivers high scalability, near 100% availability, and powerful read-and-write performance required for many data-heavy use cases. However, many developers and administrators who are new to this NoSQL database often encounter several challenges that can impact its performance.

As a SaaS leader, you know that the more metrics, insights, and analytics you add to your products, the more engagement you’ll have – and the stickier your product will become with customers. At what point do you decide to keep building your analytics in-house or invest in an embedded analytics solution? Read our Build vs. Buy Analytics guide to learn: Top 4 benefits of embedded analytics A quick cost comparison of in-house analytics development vs embedded analytics 10 considerations to help yo

Are you thinking of adding enhanced data matching and relationship detection to your product or service? Do you need to know more about what to look for when assessing your options? The Senzing Entity Resolution Buyer’s Guide gives you step-by-step details about everything you should consider when evaluating entity resolution technologies. You’ll learn about use cases, technology and deployment options, top ten evaluation criteria and more.

If you’re considering migrating from DataStax Enterprise (DSE) to open source Apache Cassandra®, our comprehensive guide is tailored for architects, engineers, and IT directors. Whether you’re motivated by cost savings, avoiding vendor lock-in, or embracing the vibrant open-source community, Apache Cassandra offers robust value. Transition seamlessly to Instaclustr Managed Cassandra with our expert insights, ensuring zero downtime during migration.

In embedded analytics, keeping up with the pace of innovation is challenging. Download Qrvey's guide to ensure your analytics keep pace so you can solve your user's biggest challenges, delight them, and set your product apart from the competition. The guide outlines how to use embedded analytics to: Increase user satisfaction Go to market faster Create additional opportunities to monetize your product It also shares what to look for to ensure your embedded analytics are keeping up with the lates

In the rapidly evolving healthcare industry, delivering data insights to end users or customers can be a significant challenge for product managers, product owners, and application team developers. The complexity of healthcare data, the need for real-time analytics, and the demand for user-friendly interfaces can often seem overwhelming. But with Logi Symphony, these challenges become opportunities.

The complexity of financial data, the need for real-time insight, and the demand for user-friendly visualizations can seem daunting when it comes to analytics - but there is an easier way. With Logi Symphony, we aim to turn these challenges into opportunities. Our platform empowers you to seamlessly integrate advanced data analytics, generative AI, data visualization, and pixel-perfect reporting into your applications, transforming raw data into actionable insights.

Organizations look to embedded analytics to provide greater self-service for users, introduce AI capabilities, offer better insight into data, and provide customizable dashboards that present data in a visually pleasing, easy-to-access format. To better understand the factors behind the decision to build or buy analytics, insightsoftware partnered with Hanover Research to survey IT, software development, and analytics professionals on why they make the embedded analytics choices they do.

In the rapidly-evolving world of embedded analytics and business intelligence, one important question has emerged at the forefront: How can you leverage artificial intelligence (AI) to enhance your application’s analytics capabilities? Imagine having an AI tool that answers your user’s questions with a deep understanding of the context in their business and applications, nuances of their industry, and unique challenges they face.